3 accepted papers
Diffusion models have achieved remarkable performance on a wide range of generative tasks, yet training them from scratch is notoriously resource-intensive, typically requiring millions of training images and many GPU days. Motivated by a data-centric view of this bottleneck, we adopt a condensation
Most existing spatial reasoning benchmarks focus on static or globally observable environments, failing to capture the challenges of long-horizon reasoning and memory utilization under partial observability and dynamic changes. We introduce two dynamic spatial benchmarks—locally observable maze navi
The escalating global demand for mental health services highlights the potential of Large Language Models (LLMs) in psychological counseling. However, current LLM-based approaches, particularly fine-tuned models, are constrained by data distribution biases, leading to limited therapeutic diversity a